Mobile and Cloud Centric Internet of Thingskodu.ut.ee/~srirama/talks/Keynote_RWCC_2016.pdf · •...
Transcript of Mobile and Cloud Centric Internet of Thingskodu.ut.ee/~srirama/talks/Keynote_RWCC_2016.pdf · •...
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Mobile and Cloud Centric Internet of
Things
Satish [email protected]
RWCC 2016
22nd -23rd December 2016
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• Head of Mobile & Cloud Lab, Institute of
Computer Science, University of Tartu, Estonia
http://mc.cs.ut.ee
Who am I
Satish Srirama 2
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TARTU
Pop: 100,000
Estonia pop: 1,300,000
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Academic excellence since 16321/8/2017 5
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Main Research Activities
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Outline
• Layers of Cloud-based IoT
• Mobile Web Services
• Mobile Cloud Binding Models
– Task delegation
– Code offloading
• Cloud-based IoT Data Processing
• Research Roadmap
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Cloud Computing
• Computing as a utility– Utility services e.g. water, electricity, gas etc
– Consumers pay based on their usage
• Cloud Computing characteristics – Illusion of infinite resources
– No up-front cost
– Fine-grained billing (e.g. hourly)
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Potential of Cloud Computing
• Cloud computing has emerged as one of the most prominent platforms instigating
– Enterprise applications
– Social networking applications etc.
• Now IoT is emerging as another important domain
– In realizing smart environment,
smart cities, smart healthcare etc.
• Cloud has huge potential to drive IoT
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Cloud
Computing
Mobile
Computing
Internet of
Things
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Internet of Things (IoT)
• IoT allows people and things to be connected
– Anytime, Anyplace, with Anything and Anyone,
ideally using Any path/network and Any service[European Research Cluster on IoT]
• More connected devices than people
• Cisco believes the market size will be $19
trillion by 2025
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IoT - Scenarios
• Environment Protection
• Smart Home
• Smart Healthcare
• Smart Agriculture
[Kip Compton][Perera et al, TETT 2014]
1/8/2017 Satish Srirama 11[http://www.libelium.com/improving-banana-crops-production-and-agricultural-sustainability-in-colombia-using-sensor-networks/ ]
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Internet of Things – Challenges
Sensors Tags Mobile Things
Appliances & Facilities
How to interact
with ‘things’
directly?
How to provide
energy efficient
services?
How do we
communicate
automatically?
[Chang et al, ICWS 2015]
[Chang et al, SCC 2015;
Liyanage et al, MS 2015]
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Layers of Cloud-based IoT
Sensing and smart devices
Connectivity nodes &
Embedded processing
Remote Cloud-based
processing
Proxy Storage
Processing
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Sensing and Smart Devices
• IoT Devices
– Sensors and actuators
– Motion, Temp, Light, Open/Close, Video,
Reading, Power on/off/dimm etc.
• Communication protocols
– Wireless and wired
– Protocols such as ZigBee, Z-Wave, Wi-Fi/Wi-Fi Direct, Bluetooth etc.
• Arduino & Raspberry PI
– For rapid prototyping
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Gateway/Connectivity Nodes
• Primarily deals with the sensor data acquisition and provisioning
• Embedded processing saves the communication latencies
• Predictive analytics
– Collect data only occasionally
• Mobiles can also participate
– This brings in the scope of mobile web services and mobile cloud services for IoT
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Advances in Mobile Technologies
• Mobile – The Seventh Mass Media Channel [Tomi T Ahonen]
• Embedded Hardware
– Camera, Wifi, sensors such as accelerometer,
magnetic field, etc.
• Higher data transmission and ubiquitous
access to Internet
– 3G, 4G, 5G, Wifi
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Mobile Hosts in Enterprise Service
Integration
• Web services (WS)
– Enable enterprise integration
• Mobile web services (MWS) [LA, OMA]
– Weather, search, maps etc.
WSDLUDDI
Registry
Find
Refer
DescribePublish
ServiceService
RequesterService Provider
SOAP
[Srirama et al, ICIW 2006;
Srirama, 2008]
REST /
• Mobile Social Networks in proximity [Chang et al,
ICSOC 2012; PMC 2014]UDDI - Universal Description, Discovery and Integration
WSDL - Web Services Description Language
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Light-weight Mobile Hosts for Sensor
Mediation
• Mobile Host can directly provide the collected sensor information– Data can be collected based on need
• Ideal MWS Protocol Stack – Things have improved significantly over the years
– Bluetooth Low Energy (BTLE) for local service discovery and interaction
– UDP instead of TCP
– Constrained Application
Protocol (CoAP)
– Efficient XML Interchange (EXI)
1/8/2017 Satish Srirama [Liyanage et al, MS 2015]
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Limitations with Mobiles
• Longer battery life
– Battery lasts only for 1-2 hours for continuous computing
• Same quality of experience as on desktops
– Weaker CPU and memory
– Storage capacity
• Still it is a good idea to take the support of external resources
– For building resource intensive mobile applications
– Brings in the scope for cloud computing
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Mobile Cloud Applications
• Bring the cloud infrastructure to the proximity
of the mobile user
• Mobile has significant advantage by going
cloud-aware
– Increased data storage capacity
– Availability of unlimited processing power
– PC-like functionality for mobile applications
– Extended battery life (energy efficiency)
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Mobile Cloud – Interpretation
• We should not see Mobile Cloud to be just a scenario where mobile is taking the help of a much powerful machine!!!
• We should not see cloud as just a pool of virtual machines
• Mobile Cloud based system should take advantage of some of the key intrinsic characteristics of cloud efficiently– Elasticity & AutoScaling
– Utility computing models
– Parallelization (e.g., using MapReduce)
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Mobile cloud - Binding models
Task Delegation Code Offloading
MCM
[Flores and Srirama, JSS 2014] [Flores et al, IEEE Communications Mag 2015]
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Task Delegation
• Follows traditional SOA model to invoke services
– Similar to mobile Web service client
• Typical scenarios
– Process intensive services• Face recognition, sensor mining etc.
– Data Synchronization (SyncML, Funambol, Google Sync)• Calendar, contacts etc.
• Critical challenges were (~2010)
– Cloud interoperability
– Unavailability of standards and mobile platform specific API
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Mobile Cloud Middleware
[Flores et al, MoMM 2011; Flores and Srirama, JSS 2014]
[Srirama and Paniagua, MS 2013]
[Warren et al, IEEE PC 2014]
Google Cloud
Messaging
Microsoft Push
Notification
Service
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MCM – enables
• Interoperability between different Cloud Services (IaaS, SaaS, PaaS) and Providers (Amazon, OpenStack, Eucalyptus, etc.)
• Provides an abstraction layer on top of API
• Composition of different Cloud Services
• Asynchronous communication between the device and MCM
• Means to parallelize the tasks and take advantage of Cloud’s intrinsic characteristics
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CroudSTag – Scenario
• CroudSTag takes the pictures/videos from the cloud and tries to recognize people
– Pictures/Videos are actually taken by the phone
– Processes the videos
– Recognizes people using facial recognition technologies
• Reports the user a list of people recognized in the pictures
• The user decides whether to add them or not to the social group
• The people selected by the user receive a message in facebook inviting them to join the social group
[Srirama et al, PCS 2011; SOCA 2012]
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CroudSTag [Srirama et al, PCS 2011;
SOCA 2012]
• Cloud services used
– Media storage on
Amazon S3
– Processing videos on
Elastic MapReduce
– face.com to recognize
people on facebook
– Starting social group
on facebook
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Other applications
• Zompopo [Srirama et al, NGMAST 2011]
– Intelligent calendar, by mining accelerometer sensor data
• Bakabs [Paniagua et al, iiWAS-2011]
– Managing the Cloud resources from mobile
• Sensor data analysis– Human activity recognition
– Context aware gaming
– MapReduce based sensor data analysis [Paniagua et al, MobiWIS2012]
• SPiCa: A Social Private Cloud Computing Application Framework [Chang et al, MUM 2014]
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Adaptive Workflow Mediation
Framework
• Task delegation is a reality!!!
– Cloud providers also support different platforms
• Mobile Host allows invocation of services on smartphones
• So Peer-to-Peer (P2P) communication is possible
• Extended the Mobile Host to also support workflow execution [Chang et al, ICSOC 2012; MUM 2014]
– To address challenges of discovery and quality of service (QoS) [Srirama et al, MW4SOC 2007]
– Tasks can move between mobile and middleware
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Mobile cloud - Binding models
Task Delegation Code Offloading
MCM
[Flores and Srirama, JSS 2014] [Flores et al, IEEE Communications Mag 2015]
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Code Offloading
• Also known as Cyber-foraging [M. Satyanarayanan et al, PC
2009]
• Mobile devices offload some of their heavy
work to stronger surrogate machines
– within the vicinity (Cloudlets)
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Major Components
• Mobile– Code profiler
– System profilers
– Decision engine
• Cloud based surrogate platform
• Major research challenges – What, when,
where and how to offload?
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Some of the well known frameworks
• MAUI – Manual annotations [Cuervo et al., 2010]
• CloneCloud– Code profilers & Automated process [Chun et al., 2011]
• ThinkAir– Manual annotations and scalability [Kosta et al, 2012]
• EMCO [Flores and Srirama, MCS 2013]
– Improved offloading by analysing the traces
• mCloud [Zhou et al, Cloud 2015; TSC 2016] & etc.– A context-aware offloading framework for heterogeneous mobile
cloud
• Work in controlled environments like nearby servers– However, none can be adapted for real life applications
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Challenges and technical problems
• Inaccurate code profiling– Code has non-deterministic behaviour during runtime
• Based on factors such as input, type of device, execution environment, CPU, memory etc.
– Some code cannot be profiled (e.g. REST)
• Integration complexity– Surrogate should have similar execution environment
• Dynamic configuration of the system
• Offloading scalability and offloading as a service– Should also consider about resource availability of
Cloud[Flores et al, IEEE Communications Mag 2015 ]
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Practical adaptability of offloading
Applications that can benefit became limited with increase in device capacities 35
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Multi-tenancy for code offloading
Auto-scalingbecomes a challenge
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Dynamic configuration
Vast resource allocation choices in the cloud ecosystemand the large diversity of smartphones make the context veryvariable
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Remote Cloud-based Processing -
Challenges
• Dynamic deployment of applications on cloud
– Standardization efforts from CloudML[REMICS EU FP7; MODAClouds EU FP7; Srirama et al, Cloud 2016]
• Auto-scaling & Resource provisioning
– Taking advantage of cloud heterogeneity
– Cloud cost models of fine-grained billing (e.g.
hourly) [Srirama and Ostovar, CloudCom 2014]
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IoT Data Processing on Cloud
• Enormous amounts of unstructured data
– In Zetabytes (1021 bytes) by 2020 [TelecomEngine]
– Has to be properly stored, analysed and interpreted and presented
• Big data acquisition and analytics
• Economics of Cloud Providers
– Cloud Computing providers bring a shift from high reliability/availability servers to commodity servers
– Replication of data and computation
• MapReduce [Dean and Ghemawat, ACM 2008]
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Adapting computing problems to cloud
• Designed a classification on how the algorithms can be adapted to MR [Srirama et al, FGCS 2012]
– Algorithm � single MapReduce job • Monte Carlo, RSA breaking
– Algorithm � n MapReduce jobs• CLARA (Clustering), Matrix Multiplication
– Each iteration in algorithm � single MapReduce job• PAM (Clustering)
– Each iteration in algorithm � n MapReduce jobs • Conjugate Gradient
• Applicable especially for Hadoop MapReduce
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Alternative approaches
• Restructuring algorithms into non-iterative versions
– CLARA instead of PAM [Jakovits and Srirama, Nordicloud 2013]
• Alternative MapReduce implementations that are designed to handle iterative algorithms [Jakovits and Srirama, HPCS
2014]
– E.g. Twister, HaLoop, Spark
• Alternative distributed computing models
– Bulk Synchronous Parallel model [Valiant, 1990] [Jakovits et al, HPCS 2013]
– Built a fault-tolerant BSP framework (NEWT) [Kromonov et al, HPCS
2014]
• Dynamic Algorithm Modeling Application (DAMA)
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• IoT mostly deals with streaming data– Message queues such as Apache Kafka to buffer and feed the
data into stream processing systems such as Apache Storm
– Apache Spark streaming
• How to ensure QoS aspects such as security of data?– Anonymization and Expiry of data?
• Especially for the personal data
IoT Data Processing on Cloud -
continued
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https://www.datanami.com/2015/11/30/spark-streaming-what-is-it-
and-whos-using-it/
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Fog Computing
1/8/2017 Satish Srirama 43
[Liyanage et al, PDCAT 2016]
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Scenario: Disabled Person Trying to
Avoid Crowd in Urban Areas
• Let us assume everything we discussed so far
works!
Utility Cloud Discovery Servers
Smart Objects
(things)
Proximal
Discovery
Metadata
Discovery
End of Story?
1/8/2017 Satish Srirama 44
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Real-time IoT Service Discovery [Chang et al, SCC 2015]
SCORPII Mobile
Host
SCORPII Mobile
Host
SCORPII Mobile
Host
n1
n2
n3
n5
n4
SCORPIIUtility Cloud
Side
Timestamp 1 Timestamp 2 Timestamp 3
Discovery Servers Discovery
Server
Discovery Servers
SCORPIIUtility
Cloud Side
Discover Discover
Discover
Smart
Objects
Smart Objects
Smart
Objects
1/8/2017 Satish Srirama 45
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Discovery Workflow
• Workflow approach selection
• Fuzzy sets and Cost Performance Index
1/8/2017 Satish Srirama 46
[Chang et al, SCC 2015]
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Research Roadmap - IoT
1/8/2017 Satish Srirama 47
Energy-Efficient and Cost-Efficient Connected Things
Reliable Adaptive Middleware
Big Data Acquisition &
Analytics
Domain
Specific
Service
Provisioning
• Smart Healthcare;
• Environmental Monitoring;
• Smart Cities etc.
• Elastic Cloud Processing;
• MapReduce & Apache Hadoop Ecosystem
• Machine Learning;
• Stream Data Processing;
• Service-Oriented Computing;
• Process Management;
• IoT Platforms & Fog Computing;
• Mobile Computing;
• Wireless Sensor &
Actuator Networks;
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IoT and Smart Solutions Laboratory
1/8/2017 48Satish Srirama